David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Minds and Machines 23 (1):105-121 (2013)
Language understanding is one of the most important characteristics for human beings. As a pervasive phenomenon in natural language, metaphor is not only an essential thinking approach, but also an ingredient in human conceptual system. Many of our ways of thinking and experiences are virtually represented metaphorically. With the development of the cognitive research on metaphor, it is urgent to formulate a computational model for metaphor understanding based on the cognitive mechanism, especially with the view to promoting natural language understanding. Many works have been done in pragmatics and cognitive linguistics, especially the discussions on metaphor understanding process in pragmatics and metaphor mapping representation in cognitive linguistics. In this paper, a theoretical framework for metaphor understanding based on the embodied mechanism of concept inquiry is proposed. Based on this framework, ontology is introduced as the knowledge representation method in metaphor understanding, and metaphor mapping is formulated as ontology mapping. In line with the conceptual blending theory, a revised conceptual blending framework is presented by adding a lexical ontology and context as the fifth mental space, and a metaphor mapping algorithm is proposed
|Keywords||Metaphor Ontology Conceptual blending Cognitive science Embodied cognition|
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References found in this work BETA
Michael L. Anderson (2003). Embodied Cognition: A Field Guide. Artificial Intelligence 149 (1):91-130.
Gilles Fauconnier (2002). The Way We Think: Conceptual Blending and the Mind's Hidden Complexities. Basic Books.
Gilles Fauconnier & Mark Turner (1998). Conceptual Integration Networks. Cognitive Science 22 (2):133-187.
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Citations of this work BETA
Fenrong Liu & Kaile Su (2013). Logic and AI in China: An Introduction. [REVIEW] Minds and Machines 23 (1):1-4.
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